A challenge for enumerating numbers of objects is the detection and tracking, and subsequent counting of free flying bats emerging from their roost. Thermal infrared imagers have shown broad potential for finding warm blooded animals and locating their habitats under a wide range of conditions. Boonstra, R., et al., Finding Mammals Using Far-Infrared Thermal Imaging, Journal of Mammalogy, 75(4):1063-1068, 1994. Gamer, D. L., et al., Use of Modern Infrared Thermography for Wildlife Population Surveys, Environmental Management, 19(2):233-238, 1995. Havens, K. J. and E. J. Sharp, Using Thermal Imagery in the Aerial Survey of Animals, Wildlife Society Bulletin, 26(1):17-23, 1998. Generally, imagery for these applications has been obtained from airborne platforms. Detection of candidate wildlife typically involves extracting a warm candidate from a thermally variable cluttered background. Techniques typically involve thresholding to segment out candidate areas, followed by shape and size feature analysis of candidate areas within single frames of imagery.
A large emergence of bats is typically a wildly chaotic process. The flight path is neither uniform nor unidirectional and the bats may be observed against all nature of background clutter. A technique is needed which can detect bats against realistic background clutter and does not require any assumptions on flight behavior of the bats.
Conventional image processing-based enumeration techniques have serious limitations when applied to the reality of a large emergence. Enumeration of bats emerging from a large roost of colonial bats is a particularly challenging and an ecologically useful undertaking—given the complexity and chaos of the bat flow, and the lack of reliable and accurate alternative enumeration methods. Kirkwood and Cartwright first showed the utility of stationary ground-based thermal infrared videography for the detection of bats in flight and in roosts. Kirkwood, J. J., and A. Cartwright, Behavioral Observations in Thermal Imaging of the Big Brown Bat, Eptesicus fuscus, Proceedings of the International Society for Optical Engineering, vol. 1467, Thermosense XIII:369-371, 1991.
Sabol and Hudson showed the feasibility of a semi-automated digital image processing procedure for enumerating bats in a large cave emergence using un-calibrated thermal infrared video imagery. Sabol, B. M., and M. K. Hudson, Technique Using Thermal Infrared-Imaging for Estimating Populations of Gray Bats, Journal of Mammalogy 76(4):1242-1248, 1995. They automated detection and enumeration of bats in periodically sampled frames. Image enhancement, using frame differencing, was performed prior to detection, resulting in the virtual elimination of false alarms from stationary background clutter. Flow rate (bats/minute) was then computed by applying an independent estimate of bat velocity in the image plane. Resulting counts were within a few percent of independent visual counts simultaneously made by a trained wildlife biologist.
Frank et al. used an approach similar to Sabol and Hudson (1995), employing all frames in the video stream to enumerate bats emerging from caves in central Texas. Frank, J. D. et al., Advanced Infrared Detection and Image Processing for Automated Bat Censusing, Proceedings of the International Society of Optical Engineering, vol. 5074, Infrared Technology and Applications XXIX:261-271, 2003. Frank et al. reduced background clutter by erecting a large thermally homogeneous background screen against which the bats are easily detected. This method required the assumption of a uniform unidirectional bat flight and estimates of that velocity.
A single polarity approach is used when all the candidate objects imaged have an observable constant polarity, i.e., objects are predictably and consistently different from their immediate background, not changing from frame to frame or over the duration of a capture event (episode). The single polarity approach to pixel detection is described in a paper presented to the International Optical Society. (SPIE Proceedings vol. 5811:24-33, March 2005). Subsequent use of this technique for counting bats exposed the limitations of the single polarity approach.
Melton et al. developed a technique to detect bats against realistic background clutter that does not require any assumptions on the flight behavior of the bats. Melton, R. E. et al., Poor Man's Missile Tracking Technology: Thermal IR Detection and Tracking of Bats in Flight, Proceedings of the International Society of Optical Engineering (SPIE), vol. 5811:24-33, 2005. This process iterates frame by frame resulting in the ability to track individual bats from the time they first appear until they are lost from the field of view. Sequential frames are differenced to remove stationary clutter, and thresholded to select pixels outside of the central distribution of differenced pixel values (both positive and negative). This technique has proven successful, within 2% when compared with manual counts, but only when the polarity of the tracked objects remains constant.
The Melton et al. technique requires that the bat exhibit a “hot polarity,” i.e., that it be warmer than its immediately surrounding background. While this is commonly the case, there are notable exceptions, such as imaging bats against natural terrain (rocks, trees, etc.) near sunset. Under these conditions a bat emerging from a cool cave may temporarily exhibit a neutral or even a “cold polarity” when it flies in front of a background object recently heated by the sun. The resulting polarity swap causes the tracking algorithm of Melton et al. to lose track of the flight path. In select embodiments of the present invention, a new technique extracts target signatures from a video stream able to track objects exhibiting polarity ambiguities, including changes, while providing automated detection, tracking, and enumeration of many closely spaced objects in motion, such as bats in free flight.